Any-to-Any
Transformers
Safetensors
GGUF
English
gemma4
image-text-to-text
4-bit precision
8-bit precision
bitsandbytes
conversational
Instructions to use LetheanNetwork/lemer-bk with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LetheanNetwork/lemer-bk with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("LetheanNetwork/lemer-bk") model = AutoModelForMultimodalLM.from_pretrained("LetheanNetwork/lemer-bk", device_map="auto") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] inputs = processor.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=256) print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use LetheanNetwork/lemer-bk with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf LetheanNetwork/lemer-bk:Q4_K_M # Run inference directly in the terminal: llama cli -hf LetheanNetwork/lemer-bk:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf LetheanNetwork/lemer-bk:Q4_K_M # Run inference directly in the terminal: llama cli -hf LetheanNetwork/lemer-bk:Q4_K_M
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf LetheanNetwork/lemer-bk:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf LetheanNetwork/lemer-bk:Q4_K_M
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf LetheanNetwork/lemer-bk:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf LetheanNetwork/lemer-bk:Q4_K_M
Use Docker
docker model run hf.co/LetheanNetwork/lemer-bk:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use LetheanNetwork/lemer-bk with Ollama:
ollama run hf.co/LetheanNetwork/lemer-bk:Q4_K_M
- Unsloth Desktop
- Pi
How to use LetheanNetwork/lemer-bk with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf LetheanNetwork/lemer-bk:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "LetheanNetwork/lemer-bk:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use LetheanNetwork/lemer-bk with Docker Model Runner:
docker model run hf.co/LetheanNetwork/lemer-bk:Q4_K_M
- Lemonade
How to use LetheanNetwork/lemer-bk with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull LetheanNetwork/lemer-bk:Q4_K_M
Run and chat with the model
lemonade run user.lemer-bk-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use LetheanNetwork/lemer-bk with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf LetheanNetwork/lemer-bk:Q4_K_M
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default LetheanNetwork/lemer-bk:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use LetheanNetwork/lemer-bk with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf LetheanNetwork/lemer-bk:Q4_K_M
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "LetheanNetwork/lemer-bk:Q4_K_M" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
Commit History
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docs: remove preliminary benchmarks pending official eval
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fix: update links for merged branches, add 4bit quant scores (33.6%)
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docs: add title attributes with run history to benchmark links
Snider +1: VirgilHover shows proof rounds. Future runs append to title: "65.0,64.5,66.0" Display value = avg. Under 8 proofs = needs more runs (8-PAC). Co-Authored-By: Virgil <virgil@lethean.io>8c1f9ae -
docs: merge best-per-category scores into grid, link to bf16 methodology
Snider +1: VirgilEach score links to bf16 branch benchmarks section for full methodology. Co-Authored-By: Virgil <virgil@lethean.io>c30acde -
docs: remove best-per-category from main, keep raw grid only
Snider +1: VirgilBest-per-category table moves to bf16 branch where data was measured. Main stays clean as polyglot repo. Co-Authored-By: Virgil <virgil@lethean.io>2c66984 -
docs: add MMLU-Pro best-per-category table (46.4%), 5-shot results
Snider +1: VirgilMath 80%, CS 65%, Biology 65% (0-shot). Full methodology with footnotes. Co-Authored-By: Virgil <virgil@lethean.io>cc4b6e4 -
docs: add 5-shot CoT methodology note, mlx_lm verification
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data: add Lemer(1,1) full 14-category results with transcripts
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docs: fill Lemer(1,1) scores across all 14 categories
Snider +1: VirgilMMLU-Pro bf16, think=on, temp=1.0, 20 samples per category. Overall: 36.8% (280 questions) Co-Authored-By: Virgil <virgil@lethean.io>93934f1 -
Update README.md
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fix: config.json matches HF 4-bit safetensors (BitsAndBytes NF4)
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add HF transformers 4-bit (NF4) safetensors to main
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docs: add LEM-research dataset reference
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docs: link quant table headers to branches
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data: add benchmark result stock-e2b-bf16-math-think-temp1.json
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